HIGH-THROUGHPUT ONCOGENE MUTATION DETECTION IN HUMAN CANCER


Year of Award:
2007
Award Type:
R21
Project Number:
CA126674
RFA Number:
RFA-CA-07-002
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
GARRAWAY, LEVI ALEXANDER
Other PI or Project Leader:
N/A
Institution:
DANA-FARBER CANCER INST
Cancer represents a disease of the genome; each tumor harbors a distinct set of mutations that activate oncogenes and inactivate tumor suppressor genes. In the era of targeted therapeutics, it is expected that cancer treatment decisions will increasingly be made based on tumor genetic composition as opposed to tissue of origin. However, most molecular diagnostics that detect cancer gene mutations are expensive, informative for only a single genetic locus, and adversely affected by degraded or stromally admixed genomic DNA. Thus, despite the promise of somatic cancer genetics, at the present time it remains impractical to identify critical oncogene mutations on a large scale and in a manner compatible with routine clinical use. To address these limitations, this application aims to adapt a high-throughput, mass spectrometry- based genotyping technology to detect somatic mutations in a large panel of cancer genes.' In the R21 phase, a platform based on SequenomTM iPLEX genotyping will be developed that interrogates over 600 point mutations (or small insertions/deletions) across 50 oncogenes and selected tumor genes. This platform will also be optimized for cancer gene mutation detection in genomic DNA from paraffin-embedded tumor tissue. In the R33 phase, test the feasibility of this mutation detection approach will be demonstrated in a study of a large and diverse tumor collection. Here, high-throughput oncogene mutation detection will be performed on nearly 2,700 frozen and paraffin-embedded tumors spanning many lineages, including several that have not undergone prior genomic characterization. The ability to perform high-throughput mutation detection in clinical tumor samples will enable unprecedented molecular analyses applicable to molecular epidemiology and translational oncology, including patient stratification for targeted cancer therapeutic trials. These studies therefore offer immense potential to benefit investigators and patients alike on the path to rational cancer therapeutics.